24,365 research outputs found
Perspects in astrophysical databases
Astrophysics has become a domain extremely rich of scientific data. Data
mining tools are needed for information extraction from such large datasets.
This asks for an approach to data management emphasizing the efficiency and
simplicity of data access; efficiency is obtained using multidimensional access
methods and simplicity is achieved by properly handling metadata. Moreover,
clustering and classification techniques on large datasets pose additional
requirements in terms of computation and memory scalability and
interpretability of results. In this study we review some possible solutions
A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observed in
functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior
during a scanning session. Such predictions suffer from the huge number of
brain regions sampled on the voxel grid of standard fMRI data sets: the curse
of dimensionality. Dimensionality reduction is thus needed, but it is often
performed using a univariate feature selection procedure, that handles neither
the spatial structure of the images, nor the multivariate nature of the signal.
By introducing a hierarchical clustering of the brain volume that incorporates
connectivity constraints, we reduce the span of the possible spatial
configurations to a single tree of nested regions tailored to the signal. We
then prune the tree in a supervised setting, hence the name supervised
clustering, in order to extract a parcellation (division of the volume) such
that parcel-based signal averages best predict the target information.
Dimensionality reduction is thus achieved by feature agglomeration, and the
constructed features now provide a multi-scale representation of the signal.
Comparisons with reference methods on both simulated and real data show that
our approach yields higher prediction accuracy than standard voxel-based
approaches. Moreover, the method infers an explicit weighting of the regions
involved in the regression or classification task
Data Management and Mining in Astrophysical Databases
We analyse the issues involved in the management and mining of astrophysical
data. The traditional approach to data management in the astrophysical field is
not able to keep up with the increasing size of the data gathered by modern
detectors. An essential role in the astrophysical research will be assumed by
automatic tools for information extraction from large datasets, i.e. data
mining techniques, such as clustering and classification algorithms. This asks
for an approach to data management based on data warehousing, emphasizing the
efficiency and simplicity of data access; efficiency is obtained using
multidimensional access methods and simplicity is achieved by properly handling
metadata. Clustering and classification techniques, on large datasets, pose
additional requirements: computational and memory scalability with respect to
the data size, interpretability and objectivity of clustering or classification
results. In this study we address some possible solutions.Comment: 10 pages, Late
Energy Efficiency in Cache Enabled Small Cell Networks With Adaptive User Clustering
Using a network of cache enabled small cells, traffic during peak hours can
be reduced considerably through proactively fetching the content that is most
probable to be requested. In this paper, we aim at exploring the impact of
proactive caching on an important metric for future generation networks,
namely, energy efficiency (EE). We argue that, exploiting the correlation in
user content popularity profiles in addition to the spatial repartitions of
users with comparable request patterns, can result in considerably improving
the achievable energy efficiency of the network. In this paper, the problem of
optimizing EE is decoupled into two related subproblems. The first one
addresses the issue of content popularity modeling. While most existing works
assume similar popularity profiles for all users in the network, we consider an
alternative caching framework in which, users are clustered according to their
content popularity profiles. In order to showcase the utility of the proposed
clustering scheme, we use a statistical model selection criterion, namely
Akaike information criterion (AIC). Using stochastic geometry, we derive a
closed-form expression of the achievable EE and we find the optimal active
small cell density vector that maximizes it. The second subproblem investigates
the impact of exploiting the spatial repartitions of users with comparable
request patterns. After considering a snapshot of the network, we formulate a
combinatorial optimization problem that enables to optimize content placement
such that the used transmission power is minimized. Numerical results show that
the clustering scheme enable to considerably improve the cache hit probability
and consequently the EE compared with an unclustered approach. Simulations also
show that the small base station allocation algorithm results in improving the
energy efficiency and hit probability.Comment: 30 pages, 5 figures, submitted to Transactions on Wireless
Communications (15-Dec-2016
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